3d Printing Defect Object Detection Model By Yolo Object Detection

3d Printing Defect Object Detection Model By Yolo Object Detection To address these issues, this study systematically evaluates four advanced yolo models (yolov11, yolov10, yolov9, yolov8, and yolov5) on a comprehensive dataset of extrusion defects, with a focus on balancing accuracy and efficiency. Welcome to the official repository for defect detection in 3d printing, a bachelor's thesis project focused on leveraging advanced object detection models (yolov5 and yolov11) to identify defects in 3d printed objects.

Defect Detection In 3d Printing Defect Detection Using Yolo V5 Ipynb At 635 open source 3d print object images and annotations in multiple formats for training computer vision models. 3d printing defect (v1, 2025 01 03 8:14pm), created by yolo object detection. Addressing these issues necessitates thorough research and optimization of 3d printers to enhance printing efficiency and stability. this study proposes a real time monitoring system for. Based on yolov10 deep learning framework, this paper trains an object detection model for 3d printing defects through 5800 pictures, with an accuracy rate of 87.3%. This work presents the first applied demonstration of a real time dual camera defect detection system on a low cost embedded platform for multi angle defect detection during active 3d printing and paves the way toward autonomous, closed loop 3d and 4d printing systems.

Defect Detection Object Detection Model By Yolo Based on yolov10 deep learning framework, this paper trains an object detection model for 3d printing defects through 5800 pictures, with an accuracy rate of 87.3%. This work presents the first applied demonstration of a real time dual camera defect detection system on a low cost embedded platform for multi angle defect detection during active 3d printing and paves the way toward autonomous, closed loop 3d and 4d printing systems. The findings demonstrate the superiority of yolo models in improving detection reliability, minimizing material waste, and streamlining fdm workflows, with yolov11 models setting new benchmarks for defect detection in additive manufacturing. We have proposed an enhanced yolov8 algorithm to train a defect detection model capable of identifying and evaluating defect images. to assess the feasibility of our approach, we took the extrusion 3d printing process as an application object and tailored a dataset comprising a total of 3550 images across four typical defect categories. Our framework utilizes “you only look once” (yolo), a real time object detection system, and “visual geometry group 16” (vgg 16), a convolutional neural network (cnn) model for image recognition, to accurately identify and localize under extrusion events. This study evaluates five object detection models (yolov5, yolov8, yolov10, yolov11, and rt detr) for detecting powder bed defects in wood based binder jetting. a dataset of 599.